# Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs

arXiv: https://arxiv.org/abs/2410.14057 · HTML: https://mlsanity.com/paper/2410.14057/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation.
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Placed in

- Retrieval-augmented generation / [Generation and context integration](https://mlsanity.com/rag/generation-and-context-integration/)

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Distilling Knowledge from Reader to Retriever for Question Answering](https://mlsanity.com/paper/2012.04584/) (2020)
- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](https://mlsanity.com/paper/2309.04662/) (2023)
- [Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering](https://mlsanity.com/paper/2210.01613/) (2022)
- [Guiding Neural Machine Translation with Retrieved Translation Pieces](https://mlsanity.com/paper/1804.02559/) (2018)
- [Democratizing Neural Machine Translation with OPUS-MT](https://mlsanity.com/paper/2212.01936/) (2022)
- [Entity Linking in 100 Languages](https://mlsanity.com/paper/2011.02690/) (2020)
- [DEEP: DEnoising Entity Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2111.07393/) (2021)
- [Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics](https://mlsanity.com/paper/2410.05183/) (2024)
- [Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs](https://mlsanity.com/paper/2311.15781/) (2023)
- [Code-Switching with Word Senses for Pretraining in Neural Machine Translation](https://mlsanity.com/paper/2310.14050/) (2023)
- [ParaNames: A Massively Multilingual Entity Name Corpus](https://mlsanity.com/paper/2202.14035/) (2022)

## Built on by (most cited first, 22 of 22)

- [Retrieval-Augmented Machine Translation with Unstructured Knowledge](https://mlsanity.com/paper/2412.04342/) (2024)
- [RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture Understanding](https://mlsanity.com/paper/2505.14462/) (2025)
- [DIWALI: Diversity and Inclusivity aWare cuLture specific Items for India: Dataset and Assessment of LLMs for Cultural Text Adaptation in Indian Context](https://mlsanity.com/paper/2509.17399/) (2025)
- [Are AI agents the new machine translation frontier? Challenges and opportunities of single- and multi-agent systems for multilingual digital communication](https://mlsanity.com/paper/2504.12891/) (2025)
- [Towards Style Alignment in Cross-Cultural Translation](https://mlsanity.com/paper/2507.00216/) (2025)
- [Language Fusion for Parameter-Efficient Cross-lingual Transfer](https://mlsanity.com/paper/2501.06892/) (2025)
- [Exploring the Translation Mechanism of Large Language Models](https://mlsanity.com/paper/2502.11806/) (2025)
- [Locate-and-Focus: Enhancing Terminology Translation in Speech Language Models](https://mlsanity.com/paper/2507.18263/) (2025)
- [CRAT: A Multi-Agent Framework for Causality-Enhanced Reflective and Retrieval-Augmented Translation with Large Language Models](https://mlsanity.com/paper/2410.21067/) (2024)
- [Towards Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST)](https://mlsanity.com/paper/2412.18367/) (2024)
- [M4-RAG: A Massive-Scale Multilingual Multi-Cultural Multimodal RAG](https://mlsanity.com/paper/2512.05959/) (2025)
- [Incentivizing Parametric Knowledge via Reinforcement Learning with Verifiable Rewards for Cross-Cultural Entity Translation](https://mlsanity.com/paper/2604.16881/) (2026)
- [KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs](https://mlsanity.com/paper/2501.03560/) (2025)
- [HausaNLP at SemEval-2025 Task 2: Entity-Aware Fine-tuning vs. Prompt Engineering in Entity-Aware Machine Translation](https://mlsanity.com/paper/2503.19702/) (2025)
- [Team ACK at SemEval-2025 Task 2: Beyond Word-for-Word Machine Translation for English-Korean Pairs](https://mlsanity.com/paper/2504.20451/) (2025)
- [Enhancing Entity Aware Machine Translation with Multi-task Learning](https://mlsanity.com/paper/2506.18318/) (2025)
- [RL from Teacher-Model Refinement: Gradual Imitation Learning for Machine Translation](https://mlsanity.com/paper/2507.22219/) (2025)
- [Toward Machine Interpreting: Lessons from Human Interpreting Studies](https://mlsanity.com/paper/2508.07964/) (2025)
- [PAUSE: Editable Strategy Artifacts for Long-Form Cultural Story Adaptation](https://mlsanity.com/paper/2608.28633/) (2026)
- [CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems](https://mlsanity.com/paper/2609.22494/) (2026)
- [Recognized but Not Produced: A Generation Benchmark for Culturally Specific Kinship Terms](https://mlsanity.com/paper/2609.26942/) (2026)
- [Retrieve What Helps, Only When It Helps: Agentic Reinforcement Learning for Selective Retrieval-Augmented Machine Translation](https://mlsanity.com/paper/2610.19528/) (2026)

## Abstract (arXiv)

Translating text that contains entity names is a challenging task, as cultural-related references can vary significantly across languages. These variations may also be caused by transcreation, an adaptation process that entails more than transliteration and word-for-word translation. In this paper, we address the problem of cross-cultural translation on two fronts: (i) we introduce XC-Translate, the first large-scale, manually-created benchmark for machine translation that focuses on text that contains potentially culturally-nuanced entity names, and (ii) we propose KG-MT, a novel end-to-end method to integrate information from a multilingual knowledge graph into a neural machine translation model by leveraging a dense retrieval mechanism. Our experiments and analyses show that current machine translation systems and large language models still struggle to translate texts containing entity names, whereas KG-MT outperforms state-of-the-art approaches by a large margin, obtaining a 129% and 62% relative improvement compared to NLLB-200 and GPT-4, respectively.